Data envelopment analysis (DEA) is an effective tool to measure the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs. This study classifies the outputs into desirable and undesirable outputs, and then presents two DEA models, radial and non-radial models, to measure the environment efficiency for DMUs with undesirable outputs. Finally, two proposed DEA models...

data envelopment analysis (dea) is an eciency measurement tool for evaluation of similar decision making units (dmus). in dea, weights are assigned to inputs and outputs and the absolute eciency score is obtained by the ratio of weighted sum of outputs to weighted sum of inputs. in traditional dea models, this measure is also equivalent with relative eciency score which evaluates dmus in com...

Malmquist Productivity Index (MPI) is a numeric index that is of great importance in measuring productivity and its changes. In recent years, tools like DEA have been utilized for determining MPI. In the present paper, some models are recommended for calculating MPI when there are just ratio data available. Then, using DEA and DEA-R, some models are proposed under the constant returns to scale ...

Data Envelopment Analysis (DEA) has become an established approach in efficiency improving strategies in industrial and regional programming. The aim of the present paper is to propose a new and recently developed Distance Friction Minimization (DFM) model in the context of DEA in order to provide an appropriate efficiency-improving projection model. The standard DEA models, including the Charn...

data envelopment analysis (dea) measures the relative efficiency of decision making units (dmus) with multiple inputs and multiple outputs. dea-based malmquist productivity index measures the productivity change over time. we propose a dynamic dea model involving network structure in each period within the framework a dea. we have previously published the network dea (ndea) and the dynamic dea ...

Traditional DEA models do not deal with imprecise data and assume that the data for all inputs and outputs are known exactly. Inverse DEA models can be used to estimate inputs for a DMU when some or all outputs and efficiency level of this DMU are increased or preserved. this paper studies the inverse DEA for fuzzy data. This paper proposes generalized inverse DEA in fuzzy data envelopment anal...

Abstract Conventional models of data envelopment analysis (DEA) are based on the constant and variable returns-to-scale production technologies. Any optimal input output weights multiplier DEA these technologies interpreted as being most favorable for decision making unit (DMU) under assessment when latter is benchmarked against set all observed DMUs. In this paper we consider a very large clas...

In a relatively short period of time Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. DEA has been successfully applied to a host of different types of entities engaged in a wide variety of activities in many contexts worldwide. This chapter discusses the fundamental DEA models and some of their extensions.

Data Envelopment Analysis (DEA) is widely used in banking sector to measure the efficiency of the banks. This paper evaluates the performance of the banks in India using cost, revenue and profit models of DEA and comes out with a comprehensive efficiency index for banks, by combing the efficiency scores of various DEA models, using the Shannon entropy. In general, the banks included in this stu...

This study's aim is to evaluate the technical efficiency of Bangladesh's manufacturing industry. We utilized DEA analysis with selected three distinct models: CCR, BCC, and slack-based model. In this study, number firms, labour, salary/wages, industrial non-industrial cost are considered as inputs variables, gross output value set variable. Our found that Common efficient industries Manufacture...